{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real time demo - trigger word detection\n",
    "\n",
    "**\"Activate\"** is the trigger word trained for the model.\n",
    "For alternative trigger word, you need to re-train the model to recognize it. More detail refer to [Trigger word detection - v1.ipynb](./Trigger word detection - v1.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from pydub import AudioSegment\n",
    "import random\n",
    "import sys\n",
    "import io\n",
    "import os\n",
    "import glob\n",
    "import IPython\n",
    "from td_utils import *\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "# To generate wav file from np array.\n",
    "from scipy.io.wavfile import write\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use 1101 for 2sec input audio\n",
    "Tx = 5511 # The number of time steps input to the model from the spectrogram\n",
    "n_freq = 101 # Number of frequencies input to the model at each time step of the spectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use 272 for 2sec input audio\n",
    "Ty = 1375# The number of time steps in the output of our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hasee\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.models import Model, load_model, Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Input, Masking, TimeDistributed, LSTM, Conv1D\n",
    "from keras.layers import GRU, Bidirectional, BatchNormalization, Reshape\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: model\n",
    "\n",
    "def model(input_shape):\n",
    "    \"\"\"\n",
    "    Function creating the model's graph in Keras.\n",
    "    \n",
    "    Argument:\n",
    "    input_shape -- shape of the model's input data (using Keras conventions)\n",
    "\n",
    "    Returns:\n",
    "    model -- Keras model instance\n",
    "    \"\"\"\n",
    "    \n",
    "    X_input = Input(shape = input_shape)\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    \n",
    "    # Step 1: CONV layer (≈4 lines)\n",
    "    X = Conv1D(filters=196, kernel_size=15, strides=4)(X_input)                                 # CONV1D\n",
    "    X = BatchNormalization()(X)                                 # Batch normalization\n",
    "    X = Activation('relu')(X)                                 # ReLu activation\n",
    "    X = Dropout(0.8)(X)                                 # dropout (use 0.8)\n",
    "\n",
    "    # Step 2: First GRU Layer (≈4 lines)\n",
    "    X = GRU(units=128, return_sequences=True)(X)                                 # GRU (use 128 units and return the sequences)\n",
    "    X = Dropout(0.8)(X)                                 # dropout (use 0.8)\n",
    "    X = BatchNormalization()(X)                                 # Batch normalization\n",
    "    \n",
    "    # Step 3: Second GRU Layer (≈4 lines)\n",
    "    X = GRU(units=128, return_sequences=True)(X)                                 # GRU (use 128 units and return the sequences)\n",
    "    X = Dropout(0.8)(X)                                 # dropout (use 0.8)\n",
    "    X = BatchNormalization()(X)                                 # Batch normalization\n",
    "    X = Dropout(0.8)(X)                                 # dropout (use 0.8)\n",
    "    \n",
    "    # Step 4: Time-distributed dense layer (≈1 line)\n",
    "    X = TimeDistributed(Dense(1, activation = \"sigmoid\"))(X) # time distributed  (sigmoid)\n",
    "\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    model = Model(inputs = X_input, outputs = X)\n",
    "    \n",
    "    return model  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model(input_shape = (Tx, n_freq))\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01)\n",
    "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a pre-train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('./models/tr_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detect trigger word functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_triggerword_spectrum(x):\n",
    "    \"\"\"\n",
    "    Function to predict the location of the trigger word.\n",
    "    \n",
    "    Argument:\n",
    "    x -- spectrum of shape (freqs, Tx)\n",
    "    i.e. (Number of frequencies, The number time steps)\n",
    "\n",
    "    Returns:\n",
    "    predictions -- flattened numpy array to shape (number of output time steps)\n",
    "    \"\"\"\n",
    "    # the spectogram outputs  and we want (Tx, freqs) to input into the model\n",
    "    x  = x.swapaxes(0,1)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    predictions = model.predict(x)\n",
    "    return predictions.reshape(-1)\n",
    "\n",
    "def has_new_triggerword(predictions, chunk_duration, feed_duration, threshold=0.5):\n",
    "    \"\"\"\n",
    "    Function to detect new trigger word in the latest chunk of input audio.\n",
    "    It is looking for the rising edge of the predictions data belongs to the\n",
    "    last/latest chunk.\n",
    "    \n",
    "    Argument:\n",
    "    predictions -- predicted labels from model\n",
    "    chunk_duration -- time in second of a chunk\n",
    "    feed_duration -- time in second of the input to model\n",
    "    threshold -- threshold for probability above a certain to be considered positive\n",
    "\n",
    "    Returns:\n",
    "    True if new trigger word detected in the latest chunk\n",
    "    \"\"\"\n",
    "    predictions = predictions > threshold\n",
    "    chunk_predictions_samples = int(len(predictions) * chunk_duration / feed_duration)\n",
    "    chunk_predictions = predictions[-chunk_predictions_samples:]\n",
    "    level = chunk_predictions[0]\n",
    "    for pred in chunk_predictions:\n",
    "        if pred > level:\n",
    "            return True\n",
    "        else:\n",
    "            level = pred\n",
    "    return False\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Record audio stream from mic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "chunk_duration = 0.5 # Each read length in seconds from mic.\n",
    "fs = 44100 # sampling rate for mic\n",
    "chunk_samples = int(fs * chunk_duration) # Each read length in number of samples.\n",
    "\n",
    "# Each model input data duration in seconds, need to be an integer numbers of chunk_duration\n",
    "feed_duration = 10\n",
    "feed_samples = int(fs * feed_duration)\n",
    "\n",
    "assert feed_duration/chunk_duration == int(feed_duration/chunk_duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_spectrogram(data):\n",
    "    \"\"\"\n",
    "    Function to compute a spectrogram.\n",
    "    \n",
    "    Argument:\n",
    "    predictions -- one channel / dual channel audio data as numpy array\n",
    "\n",
    "    Returns:\n",
    "    pxx -- spectrogram, 2-D array, columns are the periodograms of successive segments.\n",
    "    \"\"\"\n",
    "    nfft = 200 # Length of each window segment\n",
    "    fs = 8000 # Sampling frequencies\n",
    "    noverlap = 120 # Overlap between windows\n",
    "    nchannels = data.ndim\n",
    "    if nchannels == 1:\n",
    "        pxx, _, _ = mlab.specgram(data, nfft, fs, noverlap = noverlap)\n",
    "    elif nchannels == 2:\n",
    "        pxx, _, _ = mlab.specgram(data[:,0], nfft, fs, noverlap = noverlap)\n",
    "    return pxx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plt_spectrogram(data):\n",
    "    \"\"\"\n",
    "    Function to compute and plot a spectrogram.\n",
    "    \n",
    "    Argument:\n",
    "    predictions -- one channel / dual channel audio data as numpy array\n",
    "\n",
    "    Returns:\n",
    "    pxx -- spectrogram, 2-D array, columns are the periodograms of successive segments.\n",
    "    \"\"\"\n",
    "    nfft = 200 # Length of each window segment\n",
    "    fs = 8000 # Sampling frequencies\n",
    "    noverlap = 120 # Overlap between windows\n",
    "    nchannels = data.ndim\n",
    "    if nchannels == 1:\n",
    "        pxx, _, _, _ = plt.specgram(data, nfft, fs, noverlap = noverlap)\n",
    "    elif nchannels == 2:\n",
    "        pxx, _, _, _ = plt.specgram(data[:,0], nfft, fs, noverlap = noverlap)\n",
    "    return pxx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Audio stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_audio_input_stream(callback):\n",
    "    stream = pyaudio.PyAudio().open(\n",
    "        format=pyaudio.paInt16,\n",
    "        channels=1,\n",
    "        rate=fs,\n",
    "        input=True,\n",
    "        frames_per_buffer=chunk_samples,\n",
    "        input_device_index=0,\n",
    "        stream_callback=callback)\n",
    "    return stream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-..----------------------------------.----------------------------------------------------------------------."
     ]
    }
   ],
   "source": [
    "import pyaudio\n",
    "from queue import Queue\n",
    "from threading import Thread\n",
    "import sys\n",
    "import time\n",
    "\n",
    "\n",
    "# Queue to communiate between the audio callback and main thread\n",
    "q = Queue()\n",
    "\n",
    "run = True\n",
    "\n",
    "silence_threshold = 100\n",
    "\n",
    "# Run the demo for a timeout seconds\n",
    "timeout = time.time() + 0.5*60  # 0.5 minutes from now\n",
    "\n",
    "# Data buffer for the input wavform\n",
    "data = np.zeros(feed_samples, dtype='int16')\n",
    "\n",
    "def callback(in_data, frame_count, time_info, status):\n",
    "    global run, timeout, data, silence_threshold    \n",
    "    if time.time() > timeout:\n",
    "        run = False        \n",
    "    data0 = np.frombuffer(in_data, dtype='int16')\n",
    "    if np.abs(data0).mean() < silence_threshold:\n",
    "        sys.stdout.write('-')\n",
    "        return (in_data, pyaudio.paContinue)\n",
    "    else:\n",
    "        sys.stdout.write('.')\n",
    "    data = np.append(data,data0)    \n",
    "    if len(data) > feed_samples:\n",
    "        data = data[-feed_samples:]\n",
    "        # Process data async by sending a queue.\n",
    "        q.put(data)\n",
    "    return (in_data, pyaudio.paContinue)\n",
    "\n",
    "stream = get_audio_input_stream(callback)\n",
    "stream.start_stream()\n",
    "\n",
    "\n",
    "try:\n",
    "    while run:\n",
    "        data = q.get()\n",
    "        spectrum = get_spectrogram(data)\n",
    "        preds = detect_triggerword_spectrum(spectrum)\n",
    "        new_trigger = has_new_triggerword(preds, chunk_duration, feed_duration)\n",
    "        if new_trigger:\n",
    "            sys.stdout.write('1')\n",
    "except (KeyboardInterrupt, SystemExit):\n",
    "    stream.stop_stream()\n",
    "    stream.close()\n",
    "    timeout = time.time()\n",
    "    run = False\n",
    "        \n",
    "stream.stop_stream()\n",
    "stream.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stream.stop_stream()\n",
    "stream.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print the mean magnitude for each chunk of audio data\n",
    "You can play around it to find a suitable `silence_threshold` for the previous demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63.58625850340136\n",
      "133.60934240362812\n",
      "131.91519274376418\n",
      "73.27936507936508\n",
      "49.34848072562358\n",
      "52.11369614512471\n",
      "54.10943310657596\n",
      "50.06793650793651\n",
      "51.05619047619047\n",
      "52.82884353741497\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import pyaudio\n",
    "import numpy as np\n",
    "data_c = None\n",
    "\n",
    "\n",
    "def callback(in_data, frame_count, time_info, status):\n",
    "    global data_c\n",
    "    data_c = np.frombuffer(in_data, dtype='int16')\n",
    "    print(np.abs(data_c).mean())\n",
    "    return (in_data, pyaudio.paContinue)\n",
    "\n",
    "stream = pyaudio.PyAudio().open(\n",
    "    format=pyaudio.paInt16,\n",
    "    channels=1,\n",
    "    rate=fs,\n",
    "    input=True,\n",
    "    frames_per_buffer=chunk_samples,\n",
    "    input_device_index=0,\n",
    "    stream_callback=callback)\n",
    "stream.start_stream()\n",
    "time.sleep(5.1)\n",
    "stream.stop_stream()\n",
    "stream.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x250ff425978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pxx = plt_spectrogram(data_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "write('./demo/test.wav', 44100, data_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IPython.display.Audio(\"./demo/test.wav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
